Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling
Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling
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DOI：
10.1137/17m1149250
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
K. Joyce;Johnathan M. Bardsley;A. Luttman
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作者：
K. Joyce;Johnathan M. Bardsley;A. Luttman
平移不变成像系统的点扩散函数（PSF）是其脉冲响应，通常不能直接测量。高能 X 射线照相就是这种情况，必须根据与脉冲响应间接相关的校准物体的图像来估计。当假设 PSF 具有径向对称性时，可以根据不透明直边的图像来估计它。我们使用非参数贝叶斯方法，其中 PSF 的先验概率密度被建模为高斯马尔可夫随机场，并以一种新颖的方式合并径向对称性。马尔可夫链蒙特卡罗后验估计是通过采用最近开发的吉布斯采样算法的改进来进行的，称为部分折叠吉布斯采样。此外，我们提出的算法被证明能够满足目标密度的不变性。最后，我们证明了这些方法对从美国能源部内华达国家安全站点的高能 X 射线诊断系统获得的射线照相数据的有效性。
The point spread function (PSF) of a translation invariant imaging system is its impulse response, which cannot always be measured directly. This is the case in high energy X-ray radiography, and it must be estimated from images of calibration objects indirectly related to the impulse response. When the PSF is assumed to have radial symmetry, it can be estimated from an image of an opaque straight edge. We use a non-parametric Bayesian approach, where the prior probability density for the PSF is modeled as a Gaussian Markov random field and radial symmetry is incorporated in a novel way. Markov Chain Monte Carlo posterior estimation is carried out by adapting a recently developed improvement to the Gibbs sampling algorithm, referred to as partially collapsed Gibbs sampling. Moreover, the algorithm we present is proven to satisfy invariance with respect to the target density. Finally, we demonstrate the efficacy of these methods on radiographic data obtained from a high-energy X-ray diagnostic system at the U.S. Department of Energy's Nevada National Security Site.